Computer-assisted image-based risk analysis and planning in lung surgery - a review

Area of Science:

• Medical Imaging
• Surgical Planning
• Computer-Aided Diagnosis

Background:

• Lung surgery requires accurate risk assessment and planning.
• Traditional methods may lack detailed pre-operative insights.
• Image-based analysis offers potential for improved surgical outcomes.

Purpose of the Study:

• To provide an overview of computer-assisted, image-based methods for lung surgery.
• To present novel computed tomography (CT)-based algorithms for surgical risk analysis and planning.
• To discuss the impact of these methods on surgical decision-making and guidance.

Main Methods:

• Utilizing heuristic and knowledge-based image processing algorithms.
• Applying segmentation, quantification, and visualization techniques to lung CT images.
• Developing and evaluating CT-based algorithms for surgical applications.

Main Results:

• Demonstrated the application of CT-based algorithms for risk assessment in lung surgery.
• Showcased quantitative assessment of resection strategies using image analysis.
• Highlighted the utility of these methods for surgical guiding and planning.

Conclusions:

• Computer-assisted image-based methods, particularly CT-based algorithms, enhance lung surgery planning and risk assessment.
• These techniques provide quantitative insights crucial for optimizing resection strategies.
• Future advancements may involve deep-learning-based artificial intelligence (AI) for further improvements.